{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "std::cout are redirected to python::stdout\n",
      "std::cerr are redirected to python::stderr\n",
      "2020-08-05 23:46:14.760 [HKU-I] - Loading market information... [loadBaseInfo]\n",
      "2020-08-05 23:46:14.760 [HKU-I] - Loading stock type information... [loadBaseInfo]\n",
      "2020-08-05 23:46:14.761 [HKU-I] - Loading stock information... [loadBaseInfo]\n",
      "2020-08-05 23:46:15.021 [HKU-I] - Loading KData... [init]\n",
      "2020-08-05 23:46:15.053 [HKU-I] - Preloading all day kdata to buffer! [setKDataDriver]\n",
      "2020-08-05 23:46:20.038 [HKU-I] - 5.02s Loaded Data. [init]\n",
      "CPU times: user 5.34 s, sys: 350 ms, total: 5.69 s\n",
      "Wall time: 5.7 s\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%time from hikyuu.interactive import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = get_kdata('sh000001', -100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "UnicodeDecodeError",
     "evalue": "'utf-8' codec can't decode byte 0x9c in position 133: invalid start byte",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-0dca45fb1a27>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"temp\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'wb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0x9c in position 133: invalid start byte"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "with open(\"temp\", 'wb') as f:\n",
    "    pickle.dump(k, f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "hku_save(k, \"temp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "k2 = KData()\n",
    "hku_load(k2, \"temp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "k2.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
